Vertica Reviews

This solution is used as part of our data warehouse solution. We have some customer indexing content in this Vertica product. Vertica is a relational database that is used as part of our data warehouse implementation.

Vertica is a great product because customers can compress and code data. The infrastructure that data warehouse solutions need is a commodity server so that customers don't have to invest in infrastructure. Vertica is a column oriented database so the response of queries is very fast. 

In a future release, we would like to have artificial intelligence capabilities like neural networks. Customers are demanding this type of analytics.

We have been using this solution for six years. 

This is a stable solution.

This is a scalable solution. 

The customer service for this solution is good. 

Neutral

The initial setup is straightforward. 

It is important for those considering this solution to have some experience in database management solutions because Vertica is a relational database. Knowledge regarding SQL and any other database related skills is very important in order to implement or use this product correctly.

I would rate this solution a nine out of ten. 

Our use case is a typical data warehouse. We just use the data warehouse for reporting and the storage of data. Our users are the staff team who do the reporting and data analysis.

The feature I like best is performance. We use Red Tool and Red Job for the data warehouse and reporting. It's perfect. Performance is good, and it can return ad hoc queries very quickly. Of course, it's a cluster, so it's easy to scale.

It's hard to make it slow for a small data volume. For large volumes, it's hard to make it work. It's also hard to make it faster, and to make it scale.

I have been using Vertica for about five years.

It's a stable solution.

Vertica is a scalable solution.

Technical support is good, and they react quickly.

The initial setup is okay. You will need some knowledge and some training. I'd say learning takes a couple of months. We use one person to maintain the database side. With the DevOps team, everyone has a different role. But for our database, it's just one person.

The price is reasonable. We use a pay per license model. Firstly, you need to buy a license. After that, you mainly pay the annual support fee of around 20% or 25%. I think their prices are quite reasonable.

We tried to use data lake kind of stuff for machine learning, but for the key functionality of the data warehouse, it's great. Personally, I feel they are over-marketing the machine learning feature and for something like the semi-structured data. But for the data warehouse, it's truly a good solution. I want to recommend it highly.

I would tell potential users that it's hard to make it slow for small data volumes. For large volumes, it's hard to make it work, make it faster, and make it scale. Depending on your workload and your use case, you need to first purchase the Red Tool. After that, you need to follow the best practices to have an efficient design.

On a scale from one to ten, I would give Vertica a nine.

The solution is a BI solution that includes machine learning. Our company is involved in the distribution of water and we use it to capturing data for several points and to discover where there might have been a loss of drinkable water. 

There is a problem with the water distribution because the company that I'm working for has an index of 32% of water loss during the process of the distribution. These losses can be from a different source. It can be from leakage, it can be an error or on the meter read which can have many issues, sometimes the problem occurs in different hours depending on the pressure of the water network.  We need to use artificial intelligence to collect millions of the data points to detect where the problem might be coming from.

The solution has great capabilities. The tool that instructs the internal database forward is easy to use and is very powerful. 

The product could be less expensive and could benefit from a better marketing strategy. 
In a future release, I would like to have one application to help create intelligent models.

We have been testing and developing the solution for two years.

We did not have any technical problem with the solution.

The solution has great scalability. We started with one terabyte of compressed data, this is a lot of data and we never had problems with the scalability. You can have hundreds of terabytes with the solution if you want, it all depends on your needs.

The customer service is very good. They could improve on their expertise and knowledge of bigger projects and their support for them. Some of their information about collecting data I was not satisfied with their help. They could improve on customer service a bit.

I rate the technical support an eight out of ten.

We currently use IDOL as well as Vertica.

The product is not easy to set up because you need a lot of training. It has less to do about the product itself, but the knowledge on how to use it. For example, the spreadsheet product Excel, If you don't know mathematics, you will have difficulty to make big Excel model and that's the same with the Vertica. It's just only a tool and depending on your capabilities to design what you need. 

We are the developers of a solution and it is a very sophisticated project. It's something that we spent two years to develop. We already tested it and we are only waiting for the customer to try it and then purchase it. It has taken some time to implement the solution to the way we wanted for our company.

It's difficult today to compete with open-source solutions. In these areas, there is a lot of competition and the price of this solution is a bit pricy.

We use another product called IDOL and use them both together as our solution. Sometimes you use both or sometimes you use each one separately. The two products are machine learning products but with different uses. IDOL has a more developed application and is much bigger than in Vertica. 

This solution is used by several big companies such as Bank of America, Uber, and Facebook. Where you need a BI with intelligence. We use the solution because it is very good, you can make interconnections with anything to collect the data, any type of data. I have tried other products and they did not fit as well as this one did, I recommend Vertica.

I rate Vertica a nine out of ten.

The fact that it is a columnar database is valuable. Columnar storage has its own benefit with a large amount of data. It's superior to most traditional relational DB when dealing with a large amount of data. We believe that Vertica is one of the best players in this realm.

Large-volume queries are executed in a relatively short amount of time, so that we could develop reports that consume data in Vertica.

Speed: It's already doing what it is supposed to do in terms of speed but still, as a user, I hope it gets even faster.

Specific to our company, we do store the data both in AWS S3 and Vertica. For some batch jobs, we decided to create a Spark job rather than Vertica operations for speed and/or scalability concerns. Maybe this is just due to the computation efficiency between SQL operations vs. a programmatic approach. Even with some optimization (adding projections for merge joins and grouped by pipelined), it's still taking a longer time than a Spark job in some cases.

I have personally used it for about 2.5 years.

I have not recently encountered any stability issues; we have good health checks/monitoring around Vertica now.

I have not encountered any scalability issues; I think it's scalable.

N/A; don't have much experience on this.

We do have some pipelines accessing raw data directly and process it as a batch Spark job. Why? I guess it's because the type of operations we do can be done easily in code vs. SQL.

I would recommend using Vertica for those people/teams having large denormalized fact tables that need to be processed efficiently. I worked around optimizing the query performance dealing with projections, merge joins and groupby pipelines. It paid off at the end.

Scale-out, analytical functions, ML.

We are an HP partner. A SQL-based compute platform like Vertica enables far less human overhead in operations and analytics.

More ML, both data prep, models, evaluation and workflow.  Improved support for deep analytics/ predictive modelling with machine learning algorithms. This area of analytics need a stack of functionality in order to support the scenario. The needed functionality includes:

• Data preparation. Scaling, centering, removing skewness, gap filling, pivoting, feature selection and feature generation
• Algorithms/models. Non-linear models in general. More specifically, penalized models, tree/rule-based models (incl. ensambles), SVM, MARS, Neural networks, K-nearest neighbours, Naïve bayes, etc.
• Support the concept of a “data processing pipeline” with data prep. + model. One would typically use “a pipeline” as the overall logical unit used to produce predictions/scoring.
• Automatic model evaluation/tuning. With algorithms requiring tuning, support for automated testing of different settings/tuning parameters is very useful. Should include (k fold) cross validation and bootstrap for model evaluation
• Some sort of hooks to use external models in a pipeline i.e. data prep in Vertica + model from Spark/R. 
• Parity functionality for the Java SDK compared to C++. Today the C++ SDK is the most feature rich. The request is to bring (and keep) the Java SDK up to feature parity with C++.
• Streaming data and notifications/alerts. Streaming data is starting to get well supported with the Kafka integration. Now we just need a hook to issue notifications on streaming data. That is, running some sort of evaluation on incoming records (as they arrive to the Vertica tables) and possibly raising a notification.

Two years.

No, not really.

No.

No.

Postgresql, MySQL, SQL Server. Switched because of scalability and reliability, analytics functionality. V being a better engineered product.

Straightforward. Good docs helped a lot.

Its reasonably priced for non-trivial data problems.

Yes, Hadoop / Spark, SQL Server.

See additional functionality above.

We use Vertica as our primary data warehouse. It works well, relatively, most of the time.

I just expect it to work and be serviceable. When we ran into issues, there seemed to be a lot of different opinions for how to resolve the issues and that was the feedback I gave to them. You talked to one tech, you talk to a different tech they had a much different approach. That was a big frustration point for us.

The upgrade path and which way we should go. So at the end it created a lot of confusion for us, so I wouldn't upgrade it again lightly. We're going to remain on it for the next year, but we'll probably re-evaluate at that point if we want to continue with Vertica or something else.

It's been stable since November and before that, to be fair, it was stable for quite a while.

The reason we like Hadoop and others is because they scale up, pricing doesn't scale up at the same level. Vertica is a license per terabyte product. They do give you discounts the more volume you get, but it adds up over time fast. We could scale at a lower cost with than other solutions.

Scaling was a pain point. Getting recommendation on how to set it up ultimately to provide the best performance, how many notes, other things. We got different answers from them.

We use MongoDB for some of our other internal production apps. It's a lot more involved and more complex than we like to go for a, just standard data warehouse, but we might look at Hadoop or similar for that.

There's a lot of complexities with the upgrade and costs of data failures. That was last year. It was kind of good that I forgot about those pain points.

I would recommend that they highly evaluate all their options. If they're just going to run a small data warehouse, it's probably not a bad solution. If it's something they know is going to grow dramatically and unpredictably? I don't know. I would evaluate hard.

The most valuable feature is the merge function, which is essentially the upsert function. It's become our ELT pattern. Previously, when we used the ETL tool to manage upserts, the load time was significantly longer. The merge function load time is pretty much flat relative to the volume of records processed.

HP Vertica has helped us democratize data, making it available to users across the organization.

We've had issues with query time taking longer than expected for our volume of data. However, this is due to not understanding the characteristics of the database and how to better tune its performance.

We've been using HP Vertica for three years, but only in the last year have we really started to leverage it more. We're moving to a clustered environment to support the scale out of our data warehouse.

We use it as the database for the our data warehouse. In it's current configuration, we use it as a single node, but we're moving to a clustered environment, which is what the vendor recommends.

We had no issues with the deployment.

We've had no issues with the stability.

We've had no issues scaling it.

I'd rate technical support as low to average. The tech support provides the usual canned response. We've had to learn most of how to harness the tool on our own.

I haven't used anything similar.

HP Vertica was in place when I joined the company, but it wasn't used as extensively as it is now.

We implemented it in-house, I believe.

Loading into HP Vertica is straightforward, similar to other data warehouse appliance databases such as Netezza. However, tuning it for querying requires a lot more thought. It uses projections that are similar to indexes. Knowing how to properly use projections does take time. One thing to be mindful of with columnar databases is that the fewer the columns in your query, the faster the performance. The number of rows impacts query time less.

My advice would be to try out the database connecting to your ETL tools and perform time studies on the load and query times. It's a good database. It works similar to Netezza from my experience but it is a lot cheaper. Pricing is based on the size of the database.